# Leveraging GPT-4o Efficiency for Detecting Rework Anomaly in Business Processes

arXiv: https://arxiv.org/abs/2502.06918 · HTML: https://mlsanity.com/paper/2502.06918/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications](https://mlsanity.com/paper/2402.07927/) (2024)
- [Large Language Models as General Pattern Machines](https://mlsanity.com/paper/2307.04721/) (2023)
- [Are Large Language Models the New Interface for Data Pipelines?](https://mlsanity.com/paper/2406.06596/) (2024)
- [Conformance checking: A state-of-the-art literature review](https://mlsanity.com/paper/2007.10903/) (2020)
- [OCEL (Object-Centric Event Log) 2.0 Specification](https://mlsanity.com/paper/2403.01975/) (2024)
- [Trace Encoding in Process Mining: a survey and benchmarking](https://mlsanity.com/paper/2301.02167/) (2023)
- [DABL: Detecting Semantic Anomalies in Business Processes Using Large Language Models](https://mlsanity.com/paper/2406.15781/) (2024)
- [Tailoring Machine Learning for Process Mining](https://mlsanity.com/paper/2306.10341/) (2023)
- [Detecting Anomalous Events in Object-centric Business Processes via Graph Neural Networks](https://mlsanity.com/paper/2403.00775/) (2024)
- [Challenges of Anomaly Detection in the Object-Centric Setting: Dimensions and the Role of Domain Knowledge](https://mlsanity.com/paper/2407.09023/) (2024)

## Abstract (arXiv)

This paper investigates the effectiveness of GPT-4o-2024-08-06, one of the Large Language Models (LLM) from OpenAI, in detecting business process anomalies, with a focus on rework anomalies. In our study, we developed a GPT-4o-based tool capable of transforming event logs into a structured format and identifying reworked activities within business event logs. The analysis was performed on a synthetic dataset designed to contain rework anomalies but free of loops. To evaluate the anomaly detection capabilities of GPT 4o-2024-08-06, we used three prompting techniques: zero-shot, one-shot, and few-shot. These techniques were tested on different anomaly distributions, namely normal, uniform, and exponential, to identify the most effective approach for each case. The results demonstrate the strong performance of GPT-4o-2024-08-06. On our dataset, the model achieved 96.14% accuracy with one-shot prompting for the normal distribution, 97.94% accuracy with few-shot prompting for the uniform distribution, and 74.21% accuracy with few-shot prompting for the exponential distribution. These results highlight the model's potential as a reliable tool for detecting rework anomalies in event logs and how anomaly distribution and prompting strategy influence the model's performance.